Mitochondrial Function and Diabetes: Consequences for Skeletal and Cardiac Muscle Metabolism

SIGNIFICANCE: An early hallmark in the development of type 2 diabetes is the resistance to the effect of insulin in skeletal muscle and in the heart. Since mitochondrial function was found to be diminished in patients with type 2 diabetes, it was suggested that this defect might be involved in the etiology of insulin resistance. Although several hypotheses were suggested, yet unclear is the mechanistic link between these two phenomena. Recent advances: Herein, we review the evidence for disturbances in mitochondrial function in skeletal muscle and the heart in the diabetic state. Also the mechanisms involved in improving mitochondrial function are considered and, whenever possible, human data is cited. Reported evidence shows that interventions that improve skeletal muscle mitochondrial function also improve insulin sensitivity in humans. In the heart, available data from animal studies suggests that enhancement of mitochondrial function can reverse aging-induced changes in heart function, and can be protective against cardiomyopathy and heart failure. FUTURE DIRECTIONS: Mitochondria and their functions can be targeted with the aim of improving skeletal muscle insulin sensitivity and cardiac function. However, human clinical intervention studies are needed to fully substantiate the potential of mitochondria as a target to prevent cardiometabolic disease

Can biological quantum networks solve NP-hard problems?

There is a widespread view that the human brain is so complex that it cannot be efficiently simulated by universal Turing machines. During the last decades the question has therefore been raised whether we need to consider quantum effects to explain the imagined cognitive power of a conscious mind. This paper presents a personal view of several fields of philosophy and computational neurobiology in an attempt to suggest a realistic picture of how the brain might work as a basis for perception, consciousness and cognition. The purpose is to be able to identify and evaluate instances where quantum effects might play a significant role in cognitive processes. Not surprisingly, the conclusion is that quantum-enhanced cognition and intelligence are very unlikely to be found in biological brains. Quantum effects may certainly influence the functionality of various components and signalling pathways at the molecular level in the brain network, like ion ports, synapses, sensors, and enzymes. This might evidently influence the functionality of some nodes and perhaps even the overall intelligence of the brain network, but hardly give it any dramatically enhanced functionality. So, the conclusion is that biological quantum networks can only approximately solve small instances of NP-hard problems. On the other hand, artificial intelligence and machine learning implemented in complex dynamical systems based on genuine quantum networks can certainly be expected to show enhanced performance and quantum advantage compared with classical networks. Nevertheless, even quantum networks can only be expected to efficiently solve NP-hard problems approximately. In the end it is a question of precision - Nature is approximate.Comment: 38 page

Optimized parameter search for large datasets of the regularization parameter and feature selection for ridge regression

In this paper we propose mathematical optimizations to select the optimal regularization parameter for ridge regression using cross-validation. The resulting algorithm is suited for large datasets and the computational cost does not depend on the size of the training set. We extend this algorithm to forward or backward feature selection in which the optimal regularization parameter is selected for each possible feature set. These feature selection algorithms yield solutions with a sparse weight matrix using a quadratic cost on the norm of the weights. A naive approach to optimizing the ridge regression parameter has a computational complexity of the order with the number of applied regularization parameters, the number of folds in the validation set, the number of input features and the number of data samples in the training set. Our implementation has a computational complexity of the order . This computational cost is smaller than that of regression without regularization for large datasets and is independent of the number of applied regularization parameters and the size of the training set. Combined with a feature selection algorithm the algorithm is of complexity and for forward and backward feature selection respectively, with the number of selected features and the number of removed features. This is an order faster than and for the naive implementation, with for large datasets. To show the performance and reduction in computational cost, we apply this technique to train recurrent neural networks using the reservoir computing approach, windowed ridge regression, least-squares support vector machines (LS-SVMs) in primal space using the fixed-size LS-SVM approximation and extreme learning machines

On Learning Navigation Behaviors for Small Mobile Robots With Reservoir Computing Architectures

Abstract—This work proposes a general Reservoir Computing (RC) learning framework which can be used to learn navigation behaviors for mobile robots in simple and complex unknown, partially observable environments. RC provides an efficient way to train recurrent neural networks by letting the recurrent part of the network (called reservoir) fixed while only a linear readout output layer is trained. The proposed RC framework builds upon the notion of navigation attractor or behavior which can be embedded in the high-dimensional space of the reservoir after learning. The learning of multiple behaviors is possible because the dynamic robot behavior, consisting of a sensory-motor sequence, can be linearly discriminated in the high-dimensional nonlinear space of the dynamic reservoir. Three learning approaches for navigation behaviors are shown in this paper. The first approach learns multiple behaviors based on examples of navigation behaviors generated by a supervisor, while the second approach learns goal-directed navigation behaviors based only on rewards. The third approach learns complex goal-directed behaviors, in a supervised way, using an hierarchical architecture whose internal predictions of contextual switches guide the sequence of basic navigation behaviors towards the goal. Index Terms—robot navigation, reservoir computing, rein-forcement learning, goal-directed navigation, recurrent neural networks, echo state network, sensory-motor coupling. I

Substrate utilization and metabolic profile in response to overfeeding with a high-fat diet in South Asian and white men:a sedentary lifestyle study

Background For the same BMI, South Asians have a higher body fat percentage, a higher liver fat content and a more adverse metabolic profile than whites. South Asians may have a lower fat oxidation than whites, which could result in an unfavorable metabolic profile when exposed to increased high-fat foods consumption and decreased physical activity as in current modern lifestyle. Objective To determine substrate partitioning, liver fat accumulation and metabolic profile in South Asian and white men in response to overfeeding with high-fat diet under sedentary conditions in a respiration chamber. Design Ten South Asian men (BMI, 18-29 kg/m(2)) and 10 white men (BMI, 22-33 kg/m(2)), matched for body fat percentage, aged 20-40 year were included. A weight maintenance diet (30% fat, 55% carbohydrate, and 15% protein) was given for 3 days. Thereafter, a baseline measurement of liver fat content (1H-MRS) and blood parameters was performed. Subsequently, subjects were overfed (150% energy requirement) with a high-fat diet (60% fat, 25% carbohydrate, and 15% protein) over 3 consecutive days while staying in a respiration chamber mimicking a sedentary lifestyle. Energy expenditure and substrate use were measured for 3 x 24-h. Liver fat and blood parameters were measured again after the subjects left the chamber. Results The 24-h fat oxidation as a percentage of total energy expenditure did not differ between ethnicities (P = 0.30). Overfeeding increased liver fat content (P = 0.02), but the increase did not differ between ethnicities (P = 0.64). In South Asians, overfeeding tended to increase LDL-cholesterol (P = 0.08), tended to decrease glucose clearance (P = 0.06) and tended to elevate insulin response (P = 0.07) slightly more than whites. Conclusions Despite a similar substrate partitioning and similar accretion of liver fat, overfeeding with high-fat under sedentary conditions tended to have more adverse effects on the lipid profile and insulin sensitivity in South Asians.</p